Media coverage of AI study predicting responses to cancer therapy ranks top 5% among published research

| Written by Scott LaFee, Susan Gammon and Greg Calhoun
Sanju Sinha, Ph.D., headshot
Sanju Sinha, Ph.D., assistant professor in the Cancer Molecular Therapeutics Program at Sanford Burnham Prebys.

Last week, Sanford Burnham Prebys and the National Cancer Institute shared findings regarding a first-of-its-kind computational tool to systematically predict patient response to cancer drugs at single-cell resolution.

Many news outlets and trade publications took note of this study and the computational tool’s potential future use in hospitals and clinics. This coverage placed the paper in the top 5% of all manuscripts ranked by Altmetric—a service that tracks and analyzes online attention of published research to improve the understanding and value of research and how it affects people and communities.

The results from the highlighted study were published on April 18, 2024, in the journal Nature Cancer.

“Our goal is to create a clinical tool that can predict the treatment response of individual cancer patients in a systematic, data-driven manner. We hope these findings spur more data and more such studies, sooner rather than later,” says first author Sanju Sinha, Ph.D., assistant professor in the Cancer Molecular Therapeutics Program at Sanford Burnham Prebys.

Here are a few of the venues that helped spread the word about this research: 

  • AP News: “Researchers … suggest that such single-cell RNA sequencing data could one day be used to help doctors more precisely match cancer patients with drugs that will be effective for their cancer.”
  • Politico, fourth story in Future Pulse newsletter: “Our hope is that being able to characterize the tumors on a single-cell resolution will enable us to treat and target potentially the most resistant and aggressive [cells], which are currently missed.”
  • “The researchers discovered that if just one clone were resistant to a particular drug, the patient would not respond to that drug, even if all the other clones responded.”
  • Inside Precision Medicine: “The model was validated by predicting the response to monotherapy and combination treatment in three independent, recently published clinical trials for multiple myeloma, breast, and lung cancer.”

“I’m very pleased with how many news outlets covered our work,” Sinha says. “It is important and will help us continue improving the tool with more data so it can one day benefit cancer patients.”

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